Retention and Graduation Rate
Analysis
Prepared for Clarion University of Pennsylvania
April 2013

In the following report, Hanover Research analyzes the retention and graduation rates of
students entering Clarion University of Pennsylvania between 2006 and 2011. More
specifically, we investigate various academic, institutional, and demographic factors that
significantly influence retention and graduation rates.

© 2013 Hanover Research | District Administration Practice

1

Hanover Research | April 2013

TABLE OF CONTENTS
Executive Summary and Key Findings ............................................................................... 3
Introduction ...........................................................................................................................3
Discussion ..............................................................................................................................3
Structure of the Report..........................................................................................................4
Key Findings ...........................................................................................................................4
Factors Promoting Retention.............................................................................................4
Factors Promoting Retention for One Additional Year......................................................5
Factors Correlated with Graduation in Four Years ............................................................5
Factors Correlated with Graduation in Five Years .............................................................6
Section I: Methodology .................................................................................................... 7
Regression Analysis................................................................................................................7
Dependent (Outcome) Variable ........................................................................................8
Independent (Explanatory) Variables ..............................................................................12
Section II: Regression Analysis ........................................................................................ 19
Regression Results: Set 1 Models ........................................................................................19
Key Findings Across Set 1 Models....................................................................................20
Model 1 (2nd Year Retention) ...........................................................................................20
Model 2 and 3 (3rd and 4th Year Retention) .....................................................................20
Model 4 (5th Year Retention) ...........................................................................................21
Model 5 (6th Year Retention) ...........................................................................................21
Regression Results: Set 2 Models ........................................................................................23
Key Findings Across Set 2 Models....................................................................................23
Model 6 (3rd Year Conditional Retention)........................................................................23
Model 7 (4th Year Conditional Retention)........................................................................24
Model 8 (5th Year Conditional Retention)........................................................................24
Model 9 (6th Year Conditional Retention)........................................................................24
Regression Results: Set 3 Models ........................................................................................26
Model 10 (Graduation within Four Years) .......................................................................26
Model 11 (Graduation within Five Years) ........................................................................27
Appendix A: Retention Rates .......................................................................................... 29
Appendix B: Graduation Rates ........................................................................................ 32

© 2013 Hanover Research | District Administration Practice

2

Hanover Research | April 2013

EXECUTIVE S UMMARY AND KEY FINDINGS
INTRODUCTION
In the following report, Hanover Research investigates the factors contributing to bachelor’s
degree-seeking students’ retention and graduation rates at Clarion University of
Pennsylvania (Clarion). In particular, we use the data supplied by Clarion to assess which
demographic, institutional, and academic variables proved the most useful predictors of
four-year and five-year graduation and second, third, fourth, fifth, and sixth year retention
for students entering in fall 2006 through 2011.
In our analysis of Clarion’s data, we developed three sets of models. The first set analyzes
factors associated with higher retention, with a focus on factors Clarion could know before a
student begins their studies at the university. Next, the second set of models analyzes
factors that can help a student be retained for an additional year, given that they were
retained through the previous year. Lastly, the third set of models analyzes factors
associated with increased graduation rates within four years and five years of enrollment at
the university.

DISCUSSION
In terms of the most notable outcomes of our analysis, we find that a student’s high school
ranking is a strong predictor of retention and graduation. A higher high school rank leads
to higher retention, and ultimately a higher likelihood of graduation within four or five years
of enrollment. This finding suggests that as Clarion seeks to boost retention and graduation
rates, the university should strive to increase its admission rates of higher ranked students.
As Clarion explained to Hanover, the university has found high school GPA to be a helpful
predictor of retention and graduation in the past. In the datasets provided to Hanover, an
extremely large number of students had missing values for the high school GPA variable
(listed as “0” in the files) and therefore high school GPA was excluded from our analysis.
Nevertheless, the finding that high school ranking (a reasonable proxy for high school GPA)
is a strong predictor of retention aligns well with Clarion’s experience.
Unfortunately, we also find that minorities, particularly black students, have a lower
likelihood of retention and graduation within four or five years at Clarion. This finding
holds true even after controlling for high school ranking, age, gender, and other observable
variables. Based on this result, Hanover recommends a review of why black students are not
being retained at higher rates at the university, with the goal of uncovering strategies to
better support these students.
Other notable takeaways from our analysis include the finding that being a student athlete
increases a student’s likelihood of retention in their third and fourth year, as well as

© 2013 Hanover Research | District Administration Practice

3

Hanover Research | April 2013

improves their chances of graduating within five years at Clarion. Perhaps unsurprisingly,
having a higher first year GPA also boosts a student’s chances of retention and graduation.
With regard to coursework, we find that if a student attempts a higher number of credits
during the spring semester, they are more likely to be retained the following year.
Additionally, if a student fails particular courses – ENG 111, MATH 050, MATH 110, and
MATH 112 – they are less likely to be retained by the university in the next year. All of these
findings offer insight into concrete predictors of retention and graduation at the university
and should assist in identifying groups of students who may need additional support while
progressing toward graduation.

STRUCTURE OF THE REPORT
The report is organized as follows: In Section I, we provide a description of the methodology
and data used to evaluate the factors affecting graduation and retention. In Section II, we
discuss and present our findings from the regression models that quantify the effects of
individual academic, institutional, and demographic factors on students’ likelihood of
retention and graduation. Further, in Appendix A and B, we present cross-tabulations of
retention and graduation rates broken down by students’ gender, ethnicity, and high school
class rank.
Before proceeding to the body of the report, below we offer a more detailed breakdown of
the key findings of our analysis.

KEY FINDINGS
FACTORS PROMOTING RETENTION
The following factors were found to be key predictors of retention:
1) Ranking in the top quarter of a high school class – For example, a student ranked in
the top 10 percent of his or her class is 19 percent more likely to be retained in their
second year at Clarion compared to a student whose ranking is within the 50-75
percent range of their high school class.1
2) Achieving higher SAT scores – While higher SAT scores were linked to an increased
likelihood of being retained, the magnitude of this effect was fairly small. For
instance, for every 100-point increase in total SAT score (Math, Reading, and Writing
combined), a student’s probability of being retained in his or her second year is
expected to increase by less than 1 percent.
Additionally, minorities, particularly black students, are found to have lower retention
rates. Holding other factors equal, our models indicate that a black student would be 12 to
19 percent less likely to be retained than a white student.
1

Note that the high school class rank measure calculates a given student’s rank as a percentage of the number of
nd
students in his or her high school class. For example, a student who is ranked 32 out of a class of 100 would
have a value of 32 percent on this measure.

© 2013 Hanover Research | District Administration Practice

4

Hanover Research | April 2013

FACTORS PROMOTING RETENTION FOR ONE ADDITIONAL YEAR
Described as “conditional retention” in our report, the following factors are associated with
an increased likelihood of being retained for an additional year if a student has completed
their previous year of study at Clarion.
1) Achieving a higher first year GPA at Clarion – A one-point increase in first year GPA
(e.g., 2.0 to 3.0) is associated with a roughly 3-4 percent increase in the likelihood of
being retained for an additional year.
2) Being a student athlete in the previous year – The chances of an athlete being
retained in the third or fourth year at Clarion is approximately 3-6 percent higher
than that of a non-athlete.
3) Attempting a higher number of credits in the previous semester – For example, a
one-credit increase in the number of credits attempted in the previous spring
semester is associated with a 4 percent increase in the likelihood of being retained
in the third year. Further, a three-credit increase would be associated with a 12
percent boost in the likelihood of being retained.
Additionally, failing the following subjects is associated with a decreased likelihood of
retention for an additional year (if the student has completed the previous year). The
estimated magnitude of this decline in likelihood of retention is 8-18 percent depending on
the course/year of retention.
1) Failing MATH 110 adversely affects third year retention.
2) Failing ENG 111 or MATH 050 adversely affects third and fourth year retention
3) Failing MATH 112 adversely affects third and fifth year retention.

FACTORS CORRELATED WITH GRADUATION IN FOUR YEARS
The following factors are associated with a higher likelihood of graduating in four years.
1) Having a higher high school ranking – For example, a student ranked in the top 10
percent of his or her high school class is roughly 12 percent more likely to graduate
in four years than a student whose ranking falls between 25-50 percent of the class.
2) Having a higher first year GPA – For every one-point increase in first year GPA, a
student’s probability of graduating within four years increases by 15 percent.
Further, being black was found to decrease a student’s likelihood of graduation in four
years by 11 percent, as compared to being white.

© 2013 Hanover Research | District Administration Practice

5

Hanover Research | April 2013

FACTORS CORRELATED WITH GRADUATION IN FIVE YEARS
The following factors are associated with a higher likelihood of graduating in five years.
1) Being female – Increases a student’s likelihood of graduating in five years by 10
percent, as compared to being a male.
2) Ranking in the top half of a high school class – For example, a student whose rank
falls between 50 and 75 percent of the high school class is 17 percent less likely to
graduate within five years than a student in the top 10 percent.
3) Being an athlete – Student athletes are nearly 9 percent more likely to graduate
within five years, as compared to non-athletes.
Finally, similar to our previous findings on retention and four-year graduation, being black
decreases the probability of a student graduating within five years by 13 percent, as
compared to being white.

© 2013 Hanover Research | District Administration Practice

6

Hanover Research | April 2013

SECTION I: METHODOLOGY
In this section, we offer an overview of the data and methodology used in our analysis.
Clarion provided Hanover with seven different datasets (a separate dataset for each year),
containing demographic, academic, and institutional information of students between the
years 2006 and 2012. More specifically, the data included the following:




Demographic data – Students’ gender, age, and race



Institutional data – Students’ starting year, graduation year, and athlete status

Academic data
o Pre-university (high school) – Students’ high school class ranking and SAT
scores
o University – Students’ first GPA, performance in specific courses (ENG 110,
ENG 111, MATH 050, MATH 110, MATH 112, and MGMT 120), number of
credits earned in each fall semester, and number of credits attempted in
each spring semester.

Our analysis is based on a master dataset that was created by combining the seven datasets
provided by Clarion. From each of the datasets, we selected students who enrolled at
Clarion in that year (either freshmen or transfer students). This provided us with a list of
9,131 unique students, who enrolled at Clarion between 2006 and 2011. For each of these
students we checked whether they appeared in the subsequent year’s dataset. Students
who appeared in the subsequent year’s dataset were marked as retained while students
who did not graduate and did not appear in the subsequent year’s dataset were marked as
not retained. Note that 299 students were dropped from our combined dataset as these
students continued to appear in the Clarion datasets even though they had been identified
as graduates of the previous year. Our final dataset available for analysis therefore contains
8,832 unique students.

REGRESSION ANALYSIS
In order to examine the impact of the various demographic, academic, and institutional
factors on retention and graduation rates, we constructed a series of linear probability
models (LPMs). Overall, we estimated 11 models grouped as Sets 1, 2, and 3, based on the
dependent (i.e., outcome) variable used.2 LPMs represent a variation of the standard
ordinary least squares (OLS) regression model used to analyze dichotomous dependent
variables. Dichotomous variables assume one of two values; in the context of the present
analysis, we assign a value of 1 to our dependent variable in cases where students meet a
specific criterion (e.g., having been retained or having graduated) and a value of 0 otherwise
(having not been retained or having not graduated).

2

As described in greater detail below, the dependent variables examined in this analysis include retention
(corresponding to Set 1), “conditional” retention (Set 2), and graduation (Set 3).

© 2013 Hanover Research | District Administration Practice

7

Hanover Research | April 2013

For each of our regression models, we provide coefficient estimates, as well as an indication
of which coefficients proved statistically significant. The coefficients of our models reveal
how much we expect the dependent variable to change when the independent variable
increases by one unit, holding all of the other independent variables in the model constant.
In the context of LPMs, the coefficients capture the change in the likelihood of retention or
graduation, depending on the model estimated. More specifically, multiplying the
coefficients by 100 percent indicates the extent to which the likelihood changes due to a
one-unit increase in the associated independent variable. For example, as seen in Figure 2.2,
a one-unit increase in first GPA leads to a 4.21 percent rise in the probability of first year
retention in Model 6.
Lastly, when presenting our results, we also include the R-squared value, which reveals the
percentage of variation in the dependent variable accounted for by the model. Using Figure
2.2 and Model 6 as an example once again, the R-squared value of 0.3284 indicates that the
model can explain 32.84 percent of the variation in retention.

DEPENDENT (OUTCOME) VARIABLE
As discussed previously, we used three types of dependent, or outcome, variables in our
regression analysis. We describe the dependent variables as follows:



Set 1 (Models 1, 2, 3, 4, and 5): The dependent variable in this set of models
indicates whether or not a student (who did not yet graduate) was retained in a
particular year of study. For instance, the second year retention of a student who
initially enrolled at Clarion in 2006 indicates whether or not the student was
retained in 2007. Similarly, the third year retention of that same student indicates
whether he or she was retained in 2008. Please note that a particular year’s
retention, as defined by this outcome variable, is independent of the students’
retention in the immediate previous year.3 Figure 1.1 below helps understand what
a particular year’s retention means for students who enrolled at Clarion between
2006 and 2011. We estimate separate models for different years of retention.

STARTING

3

Figure 1.1: Retention Years
2 YEAR
3RD YEAR
4TH YEAR
5TH YEAR
ND

6TH YEAR

YEAR

RETENTION

RETENTION

RETENTION

RETENTION

RETENTION

2006
2007
2008
2009

2007
2008
2009
2010

2008
2009
2010
2011

2009
2010
2011
2012

2010
2011
2012
--

2011
2012
---

In other words, if a student initially enrolled in 2006 but did not re-enroll in 2007, they would be marked as retained
under “second year retention.” However, if this same student re-enrolled in 2008, they would then be marked as
retained under “third year retention.” As discussed in greater detail below, the Set 2 models focus on “conditional
retention,” a measure that takes into account whether a student had been enrolled in the previous year at Clarion
in addition to whether they were enrolled in the current year.

© 2013 Hanover Research | District Administration Practice

8

Hanover Research | April 2013

STARTING

2ND YEAR

3RD YEAR

4TH YEAR

5TH YEAR

6TH YEAR

YEAR

RETENTION

RETENTION

RETENTION

RETENTION

RETENTION

2010
2011

2011
2012

2012
--

---

---

---

In Figure 1.2, we provide descriptive statistics of the dependent variables used for each
model in Set 1. For each of the student cohorts (e.g., students beginning in fall 2006,
students beginning in fall 2007, etc.), we provide the percentage of students who were
retained and not retained. The table indicates that for all cohorts, the retention rate
declines over the years. Please note that we have also provided a breakdown of retention
rates by various subgroups in Appendix A.
Figure 1.2: Distribution of Dependent Variables in Set 1 – Descriptive Statistics
STARTING
TERM

Fall 2006

Fall 2007

Fall 2008

Fall 2009

Fall 2010

Fall 2011



STATUS
Not Retained
Retained
Count
Not Retained
Retained
Count
Not Retained
Retained
Count
Not Retained
Retained
Count
Not Retained
Retained
Count
Not Retained
Retained
Count
Total

2ND YEAR

3RD YEAR

4TH YEAR

5TH YEAR

6TH YEAR

RETENTION

RETENTION

RETENTION

RETENTION

RETENTION

32%
68%
1,436
29%
71%
1,437
31%
69%
1,416
31%
69%
1,582
30%
70%
1,527
31%
69%
1,418
8,816

44%
56%
1,414
39%
61%
1,421
43%
57%
1,388
41%
59%
1,561
42%
58%
1,495
---7,279

50%
50%
1,346
44%
56%
1,365
49%
51%
1,326
48%
52%
1,499
------5,536

69%
31%
1,013
67%
33%
979
71%
29%
989
---------2,981

92%
8%
792
92%
8%
744
------------1,536

Set 2 (Models 6, 7, 8, and 9): The dependent variable for the models in Set 2
assumes a value of 1 whenever a student who was retained in a particular year is
enrolled in the following year and a value of 0 whenever a student who was retained
in a particular year is not enrolled in the following year. This is described as
“conditional” retention, as being retained in a given year is conditioned on whether
a student was retained in the previous year. For instance, in the case of a student
who started in 2006, fourth year conditional retention refers to that student being

© 2013 Hanover Research | District Administration Practice

9

Hanover Research | April 2013

retained in 2009 given that he/she was also retained in 2008. The table below
provides brief clarification of what a particular year’s conditional retention indicates.
Figure 1.3: Conditional Retention
STARTING
YEAR

2006

2007

2008

2009

2010

RD

3 YEAR CONDITIONAL

4TH YEAR CONDITIONAL

5TH YEAR CONDITIONAL

6TH YEAR CONDITIONAL

RETENTION

RETENTION

RETENTION

RETENTION

Students retained in
2007, did not graduate,
and were retained
again in 2008
Students retained in
2008, did not graduate,
and were retained
again in 2009
Students retained in
2009, did not graduate,
and were retained
again in 2010
Students retained in
2010, did not graduate,
and were retained
again in 2011
Students retained in
2011, did not graduate,
and were retained
again in 2012

Students retained in
2008, did not graduate,
and were retained
again in 2009
Students retained in
2009, did not graduate,
and were retained
again in 2010
Students retained in
2010, did not graduate,
and were retained
again in 2011
Students retained in
2011, did not graduate,
and were retained
again in 2012

Students retained in
2009, did not graduate,
and were retained
again in 2010
Students retained in
2010, did not graduate,
and were retained
again in 2011
Students retained in
2011, did not graduate,
and were retained
again in 2012

Students retained in
2010, did not graduate,
and were retained
again in 2011
Students retained in
2011, did not graduate,
and were retained
again in 2012

--

--

--

--

--

--

As the above table illustrates, conditional retention only looks at the group of students who
had been retained in the previous year. For example, if a student is marked as “not
retained” in the third year, they would not be included in the calculation of fourth year
retention (only students who had been retained in the third year would be included). This
differs from the dependent variable used in the Set 1 models, where the measure of
retention is not conditioned on whether the student was enrolled in the previous year.
Figure 1.4 on the following page provides descriptive statistics of the dependent variables
used in the Set 2 models. In general, between the third year and fifth year, the conditional
retention rate increases slightly. Note that we have also provided a breakdown of the
conditional retention rates by various sub-groups in Appendix A.

© 2013 Hanover Research | District Administration Practice

10

Hanover Research | April 2013

Figure 1.4: Distribution of Dependent Variables in Set 2 - Descriptive Statistics
STARTING
YEAR
Fall 2006

Fall 2007

Fall 2008

Fall 2009

Fall 2010



STATUS
Not Retained
Retained
Count
Not Retained
Retained
Count
Not Retained
Retained
Count
Not Retained
Retained
Count
Not Retained
Retained
Count
Total

3RD YEAR

4TH YEAR

5TH YEAR

6TH YEAR

CONDITIONAL

CONDITIONAL

CONDITIONAL

CONDITIONAL

RETENTION

RETENTION

RETENTION

RETENTION

21%
79%
956
17%
83%
1,002
19%
81%
952
18%
82%
1,074
19%
81%
1,039
5,023

11%
89%
726
9%
91%
808
10%
90%
729
10%
90%
853
---3,116

15%
85%
347
18%
82%
381
17%
83%
336
------1,064

42%
58%
96
45%
55%
95
---------191

Set 3 (Models 10 and 11): The dependent variable in this set of models indicates
whether or not a student graduated either within four years of enrollment (Model
10) or five years of enrollment (Model 11). Figure 1.5 on the following page provides
descriptive statistics of this dependent variable. As expected, the graduation rate
within five years is higher than the graduation rate within four years, as students
have had more time to complete their studies. We have also provided a breakdown
of the graduation rates by subgroups in Appendix B.
Please note that we do not have complete four-year data for students who enrolled
in 2009, 2010, and 2011. Furthermore, we also do not have five-year data for
students who enrolled in 2008. For most of these students, we do not know
whether they graduated within these years. For students in these years, we take
into account the students who are listed as graduates only. Other students are
excluded from the analysis as they are missing full four- and five-year data.

© 2013 Hanover Research | District Administration Practice

11

Hanover Research | April 2013

Figure 1.5: Distribution of Dependent Variables in Set 3 – Descriptive Statistics
STARTING
YEAR
Fall 2006

Fall 2007

Fall 2008

Fall 2009
Fall 2010
Fall 2011

STATUS

GRADUATION
WITHIN 4 YEARS

GRADUATION
WITHIN 5 YEARS

Did not graduate
Graduated
Count
Did not graduate
Graduated
Count
Did not graduate
Graduated
Count
Graduated
Count
Graduated
Count
Graduated
Count
Total

70%
30%
1,438
68%
32%
1,437
70%
30%
1,418
100%
131
100%
57
100%
11
4,492

55%
45%
1,438
52%
48%
1,437
-100%
542
100%
131
100%
57
100%
11
3,616

INDEPENDENT (EXPLANATORY) VARIABLES
We included a series of independent, or explanatory, variables in the models in order to
control for factors affecting the dependent variables of interest (retention, conditional
retention, and graduation). We provide a complete list of the independent variables used in
Figure 1.6. The table defines each independent variable, indicates the model(s) in which the
variable appears, and the variable type. Note that in general, if a variable was not found to
have a statistically significant effect on the outcome variable of interest (retention or
graduation), it was excluded from the model. For example, the “Female” variable did not
have a significant relationship with retention/graduation in Models 1-10 but did have a
significant effect on likelihood of graduation within five years. Therefore, the “Female”
variable was only included in our final version of Model 11.
Figure 1.6: Summary of Independent Variables
VARIABLES
Female

SUMMARY
Gender of students: (0) Male (1) Female

Age

Age of students

Age squared
Ethnicity

The squared value of the age of the students. This
allows us to examine whether age has a non-linear
effect on the dependent variable.
Ethnicity of students, selected from three categories:
Black, White, and “other or two or more races.”

© 2013 Hanover Research | District Administration Practice

MODEL
Model 11
Models 3, 5, 7,
and 10

VARIABLE TYPE
Categorical

Models 3 and 10

Continuous

Models 1, 2, 3, 4,
6, 8, 10, and 11

Categorical

Continuous

12

Hanover Research | April 2013

VARIABLES

High School Rank

SAT score total

SAT Math,
Reading, Writing

First GPA
ENG 111*
MATH 050*
MATH 110*
MATH 112*

SUMMARY
Student’s high school rank as a percentage of class
size. In other words, if a student was ranked 29th out
of a high school class of 100, their value for this
variable would be 29 percent. A student with a lower
ranking in the same high school class, say 45th out of
100, would have a value of 45 percent. As such,
higher values for this variable represent lower class
ranks (e.g., 45th) while lower values represent higher
class rankings (e.g., 29th).
Student’s total SAT scores (includes math, reading
and writing). We divided SAT score by 100 so that
our models show the effect of a 100 point increase in
SAT rather than a single point increase.
In cases where total SAT score was not found to a
statistically significant effect on retention or
graduation, we entered individual Math, Reading,
and/or Writing scores into the models. These scores
remained in the final models if they were found to
have a statistically significant effect on the outcome
of interest.
Student's first GPA at Clarion.
Whether student passed, failed, or did not attempt
this course.
Whether student passed, failed, or did not attempt
this course.
Whether student passed, failed, or did not attempt
this course.
Whether student passed, failed, or did not attempt
this course.

Athlete

Whether students is an athlete: (0) No (1) Yes

Credits attempted
in previous Spring
Credits earned in
previous Fall

Number of credits attempted in the previous Spring
semester
Number of credits earned in the previous Fall
semester

MODEL

VARIABLE TYPE

Models 1, 2, 3, 4,
9, 10, and 11

Categorical/Continuous

Models 1, 2, 3, 4,
6, 7, 8 , 9, and 10

Continuous

Models 5, 10,
and 11

Continuous

Models 6, 7, 8,
10, and 11

Continuous

Models 6 and 7

Categorical

Model 6 and 7

Categorical

Models 6 and 7

Categorical

Model 6, 7, and
8
Model 6, 7, and
11
Models 6, 7, 8,
and 9
Model 6

Categorical
Categorical
Continuous
Continuous

*Note that ENG 110 and MGMT 120 were excluded from our models as an extremely high percentage of students in our dataset
(90 percent or more for most cohorts) did not attempt these courses. In general, when selecting variables for inclusion in our
models, Hanover sought to use variables for which a substantial number of students had non-zero values.

Figures 1.7-1.12 present descriptive statistics for each independent variable used in the
models. While Figure 1.7 shows the distribution of students’ gender, ethnicity, and high
school ranking, Figure 1.8 and 1.9 shows descriptive statistics for some measures of
academic performance.
In our models, we used two forms of the independent variable that indicates whether a
student is an athlete. For the models in Set 2, the athlete variable refers to a student being

© 2013 Hanover Research | District Administration Practice

13

Hanover Research | April 2013

an athlete in the year immediately prior to the year in which retention is measured. For
instance, Model 7, which analyzes fourth year conditional retention, indicates the effect of a
student being an athlete in his/her third year only. By contrast, the athlete variable used in
the Set 3 models (Model 11), indicates whether the student was an athlete at any point
within the four or five years taken into account. Descriptive statistics of these alternate
forms of the athlete variable are provided in Figures 1.10 and 1.11. Finally, Figure 1.12
provides the average number of credits earned (fall) and attempted (spring) for each year of
our dataset.

© 2013 Hanover Research | District Administration Practice

14

Hanover Research | April 2013

Figure 1.7: Gender, Ethnicity, and High School Ranking – Descriptive Statistics
GENDER

FALL 2006

FALL 2007

FALL 2008

FALL 2009

FALL 2010

FALL 2011

Female

57% (n=803)

55% (n=789)

56% (n=793)

58% (n=924)

62% (n=941)

60% (n=850)

Male

43% (n=604)

45% (n=648)

44% (n=625)

42% (n=661)

38% (n=589)

40% (n=574)

Black
Other or two or more races4

6% (n=85)
3% (n=37)

7% (n=98)
3% (n=40)

9% (n=125)
4% (n=63)

9% (n=135)
4% (n=55)

9% (n=130)
5% (n=70)

9% (n=122)
6% (n=84)

White

91% (n=1281)

90% (n=1287)

87% (n=1225)

88% (n=1354)

86% (n=1276)

85% (n=1198)

0% to 10%

8% (n=108)

9% (n=111)

9% (n=114)

8% (n=104)

9% (n=117)

8% (n=98)

10% to 25%

19% (n=243)

15% (n=189)

19% (n=229)

19% (n=254)

20% (n=260)

18% (n=214)

25% to 50%

35% (n=445)

35% (n=443)

36% (n=443)

35% (n=476)

34% (n=446)

35% (n=413)

50% to 75%

27% (n=349)

29% (n=371)

27% (n=333)

26% (n=349)

26% (n=335)

25% (n=292)

75% to 100%

10% (n=132)

12% (n=156)

9% (n=109)

12% (n=162)

11% (n=145)

13% (n=157)

ETHNICITY

HIGH SCHOOL RANKING

Figure 1.8: Age, High School Rank, SAT Score, and First GPA- Descriptive Statistics
VALUES
Average of Age
Average of Rank as Percentage of Class Size
Average of SAT Total*
Average of SAT Math*
Average of SAT Reading*
Average of SAT Writing*
Average of First GPA

FALL 2006
18.93
43%
1381
462
466
455
2.44

FALL 2007
19.13
45%
1419
483
477
462
2.49

FALL 2008
19.20
42%
1412
479
474
460
2.51

FALL 2009
19.60
44%
1407
477
471
458
2.64

FALL 2010
19.70
43%
1419
478
477
464
2.66

FALL 2011
19.76
44%
1402
478
475
459
2.63

TOTAL
19.39
43%
1408
477
474
460
2.56

*Note that all SAT scores listed as “0” were recoded as missing and are not reflected in these averages.

4

Includes students who are listed as Asian, Hispanic, Native American, Pacific Islander, and from two or more races.

© 2013 Hanover Research | District Administration Practice

15

Hanover Research | April 2013

Figure 1.9: University Course Performance – Descriptive Statistics
COURSE
ENG 110

ENG 111

MATH 050

MATH 110

MATH 112

MGMT 120

VALUES
Did not attempt
Did not pass
Passed
Did not attempt
Did not pass
Passed
Did not attempt
Did not pass
Passed
Did not attempt
Did not pass
Passed
Did not attempt
Did not pass
Passed
Did not attempt
Did not pass
Passed

FALL 2006
99% (n=1429)
0% (n=)
1% (n=9)
27% (n=393)
10% (n=137)
63% (n=908)
79% (n=1135)
5% (n=75)
16% (n=228)
83% (n=1200)
4% (n=64)
12% (n=174)
76% (n=1099)
4% (n=63)
19% (n=276)
92% (n=1327)
1% (n=15)
7% (n=96)

© 2013 Hanover Research | District Administration Practice

FALL 2007
99% (n=1425)
0% (n=3)
1% (n=9)
21% (n=304)
11% (n=161)
68% (n=972)
81% (n=1165)
4% (n=62)
15% (n=210)
81% (n=1168)
4% (n=64)
14% (n=205)
77% (n=1112)
4% (n=57)
19% (n=268)
89% (n=1277)
2% (n=27)
9% (n=133)

FALL 2008
91% (n=1288)
2% (n=22)
8% (n=108)
25% (n=351)
10% (n=148)
65% (n=919)
80% (n=1141)
5% (n=64)
15% (n=213)
83% (n=1176)
5% (n=71)
12% (n=171)
76% (n=1075)
5% (n=72)
19% (n=271)
88% (n=1251)
1% (n=18)
11% (n=149)

FALL 2009
95% (n=1499)
1% (n=15)
4% (n=71)
26% (n=407)
8% (n=129)
66% (n=1049)
80% (n=1263)
4% (n=60)
17% (n=262)
87% (n=1375)
4% (n=62)
9% (n=148)
73% (n=1159)
4% (n=71)
22% (n=355)
92% (n=1458)
1% (n=13)
7% (n=114)

FALL 2010
85% (n=1304)
2% (n=37)
12% (n=189)
31% (n=481)
8% (n=120)
61% (n=929)
78% (n=1193)
5% (n=78)
17% (n=259)
89% (n=1361)
4% (n=54)
8% (n=115)
72% (n=1105)
5% (n=75)
23% (n=350)
90% (n=1375)
2% (n=31)
8% (n=124)

16

FALL 2011
84% (n=1192)
1% (n=20)
15% (n=212)
32% (n=459)
7% (n=105)
60% (n=860)
84% (n=1191)
3% (n=47)
13% (n=186)
89% (n=1263)
4% (n=51)
8% (n=110)
76% (n=1081)
4% (n=63)
20% (n=280)
90% (n=1281)
2% (n=26)
8% (n=117)

TOTAL
3,495
675
4,662
8,137
97
598
2,395
800
5,637
7,543
366
923
6,631
401
1,800
7,969
130
733

Hanover Research | April 2013

Figure 1.10: Athlete in the Year Prior to Retention – Descriptive Statistics
2006

2007

2008

2009

2010

ATHLETE STATUS
Athlete prior to 2nd year
retention
Athlete prior to 3rd year
retention
Athlete prior to 4th year
retention
Athlete prior to 5th year
retention

NO

YES

TOTAL

NO

YES

TOTAL

NO

YES

TOTAL

NO

YES

TOTAL

NO

YES

TOTAL

92%

8%

978

92%

8%

1,018

91%

9%

980

92%

8%

1,095

92%

8%

1,071

94%

6%

792

93%

7%

864

91%

9%

788

93%

7%

914

93%

7%

861

96%

4%

675

94%

6%

766

91%

9%

670

93%

7%

780

--

--

--

98%

2%

311

97%

3%

322

96%

4%

289

--

--

--

--

--

--

© 2013 Hanover Research | District Administration Practice

17

Hanover Research | April 2013

Figure 1.11: Athlete Before Graduation – Descriptive Statistics

Fall 2006
Fall 2007
Fall 2008
Fall 2009
Fall 2010
Fall 2011
Total

GRADUATION WITHIN 4 YEARS
YES
NO
TOTAL
8%
92%
1,024
8%
92%
1,063
10%
90%
1,003
8%
92%
1,134
8%
92%
1,089
---8%
92%
5,313

GRADUATION WITHIN 5 YEARS
YES
NO
TOTAL
26%
74%
322
25%
75%
342
30%
70%
323
12%
88%
760
10%
90%
863
0%
100%
1,424
11%
89%
4,034

Figure 1.12: Average Credits Earned and Attempted
2007

2008

2009

2010

2011

2012

Fall Credits Earned

14.74

14.81

14.68

14.48

14.31

14.11

Spring Credits Attempted

13.22

13.42

13.34

13.18

12.89

12.32

© 2013 Hanover Research | District Administration Practice

18

Hanover Research | April 2013

SECTION II: R EGRESSION A NALYSIS
In this section, we present the results of our regression models that help us understand the
factors that affect student retention and graduation rates at Clarion. As noted previously,
the models included in Set 1 and Set 2 examine students’ retention rates between the
second and sixth years at the university, while the models in Set 3 examine students’ fouryear and five-year graduation rates.
The results of each set of regression models are presented in Figures 2.1, 2.2., and 2.3. Each
set of models focuses on a different combination of variables. More specifically,



The models included in Set 1 (Figure 2.1) were constructed to examine the effects of
a variety of factors Clarion would be aware of prior to a student’s initial enrollment
at the university (e.g., gender, age, ethnicity, high school rank, and SAT). The
outcome variable for these Set 1 models is retention.



The Set 2 models (Figure 2.2) analyze the effects of a range of demographic, preuniversity, and university characteristics (e.g., age, gender, high school rank, SAT,
first GPA at Clarion, passage/failure of specific courses, athlete status, credits
attempted and earned) on conditional retention. As mentioned previously,
conditional retention takes into account (or is “conditioned on”) whether a student
was retained in the previous year. In other words, it only measures the retention of
students in a given year if they were also retained in the previous year.



Finally, the Set 3 models (Figure 2.3) analyze the effects of demographic, preuniversity, and university characteristics on graduation within four years and
graduation within five years.

REGRESSION RESULTS: SET 1 MODELS
We recall that Set 1, which covers Models 1 to 5, included a dependent variable showing the
proportion of students who re-enrolled at Clarion in each of the five years. We developed
separate models for the different years of retention (e.g., second year retention, third year
retention, fourth year retention, etc.) with only demographic and pre-university data
included in the models. Further, in general, the models only include variables that have a
statistically significant effect on our dependent variables5 and in the points below, we only
highlight findings that are statistically significant.
5

In the figures, statistical significance is denoted with asterisks:
*** denotes statistical significance at 1 percent – meaning that there is a less than 1 percent likelihood that the
observed effect is due to chance.
** denotes statistical significance at 5 percent – meaning that there is a less than 5 percent likelihood that the
observed effect is due to chance.
* denotes statistical significance at 10 percent – meaning that there is a less than 10 percent likelihood that the
observed effect is due to chance.

© 2013 Hanover Research | Academy Administration Practice

19

Hanover Research | April 2013

KEY FINDINGS ACROSS SET 1 MODELS
The following subsections highlight findings relative to each individual model. In terms of
broader points that can be drawn across the Set 1 models, we find that in general, the
following are associated with a higher likelihood of being retained:




Being ranked in the top quarter of a high school class
Having higher SAT scores

By contrast, minorities, and particularly black students, are less likely to be retained than
white students.

MODEL 1 (2ND YEAR RETENTION)



Relative to white students, black students and students from other racial/ethnic
backgrounds are less likely to be retained in their second year at Clarion.



Students ranked below the top 25 percent of their high school classes are less likely
to be retained in their second year.6 In fact, outside of the top 25 percent, the lower
the student’s ranking, the less likely the student is to be retained (e.g., students
whose ranking is within 50-75 percent of their high school class are less likely to be
retained than students whose ranking is within 25-50 percent of their high school
class).



Second year retention has a positive relationship with SAT scores. With every 100point increase in SAT scores, a student’s chances of being retained increase slightly
(by 0.78 percent).

MODEL 2 AND 3 (3RD AND 4TH YEAR RETENTION)

6



Black students and students from other ethnic backgrounds are less likely to be
retained in their third and fourth years than white students.



Students ranked below the top 25 percent of their high school classes are less likely
to be retained in their third and fourth years than students ranked in the top 10
percent. Similar to Model 1, outside of the top 25 percent, the lower a students’
high school rank, the less likely they are to be retained.

Note that we make this conclusion based on a combination of results. First, high school rank was separated into a
series of dummy variables where the reference category was a high school rank within the top 10 percent of a
students’ high school class. For example, the negative coefficient in Model 1 for the variable listed as “High School
Rank (25%-50%)” indicates that students whose ranking was among 25-50 percent of their high school class were
significantly less likely to be retained than students in the top 10 percent. The same could be said for students in
the 50-75 percent range and the 75-100 percent range. However, the variable measuring “High School Rank (10%25%)” was not found to a have a statistically significant effect (i.e., students in the 10-25 percent range were not
significantly less likely to be retained than students in the top 10 percent). Taken together, these results suggest
that students ranked within the top 25 percent of their high school class are more likely to be retained than
students with lower rankings.

© 2013 Hanover Research | Academy Administration Practice

20

Hanover Research | April 2013



Students with higher SAT scores are more likely to be retained in their third year at
Clarion. Once again, a 100-point increase in SAT scores increases a student’s chances
of being retained by a relatively small margin (i.e., a boost of 0.84 percent in their
likelihood of being retained in their third year). This effect was not observed with
regard to fourth year retention.



Older students are less likely to be retained in their fourth year at Clarion than
younger students. This finding did not hold true with regard to third year retention.

MODEL 4 (5TH YEAR RETENTION)



Similar to the previous models, black students and students of other ethnicities are
less likely to be retained in their fifth year than their white classmates.



Students ranked in the lower half of their high school classes (50-100 percent) are
less likely to be retained compared to students in the top half of their high school
classes.

MODEL 5 (6TH YEAR RETENTION)



Similar to Model 3 (fourth year retention), older students are less likely to be
retained in their sixth year than younger students.



While total SAT scores were not found to have an effect on sixth year retention,
higher SAT Reading scores were associated with a higher likelihood of retention and
higher SAT Writing scores were associated with a lower likelihood of retention.
Note, however, these effects appear fairly small in terms of magnitude. A 100-point
increase in Reading scores corresponds to a 3.43 percent increase in the likelihood
of being retained in the sixth year. A 100-point increase in Writing scores
corresponds to a 4.42 percent decrease in the likelihood of being retained.

© 2013 Hanover Research | Academy Administration Practice

21

Hanover Research | April 2013

Figure 2.1: Regression Output Results - Set 1
MODEL 1
ND
2 YEAR

MODEL 2
RD
3 YEAR

MODEL 3
TH
4 YEAR

MODEL 4
TH
5 YEAR

MODEL 5
TH
6 YEAR

RETENTION

RETENTION

RETENTION

RETENTION

RETENTION

Age
Age squared

-0.0584***

-0.0281*

(0.0121)

(0.017)

0.0008***
(0.0002)

Black

7

Other or two or more races

7

High School Rank (10%-25%)

8

8

High School Rank (25%-50%)

8

High School Rank (50%-75%)

High School Rank (75%-100%)
SAT Total

8

-0.1362***

-0.1864***

-0.1961***

-0.1231***

(0.022092)

(0.0261)

(0.0268)

(0.0329)

-0.0681**

-0.0711*

-0.0766*

-0.1194**

(0.032796)

(0.0409)

(0.0459)

(0.0588)

-0.0124

-0.0218

-0.0250

0.0379

(0.023772)

(0.0284)

(0.0297)

(0.0465)

-0.0718***

-0.0950***

-0.1150***

-0.0096

(0.022793)

(0.0272)

(0.0272)

(0.0424)

-0.1857***

-0.2156***

-0.2487***

-0.0722*

(0.024141)

(0.0288)

(0.0279)

(0.0425)

-0.2757***

-0.3569***

-0.3317***

-0.1442***

(0.028049)

(0.0337)

(0.0323)

(0.0464)

0.0078**

0.0084**

(0.003156)

(0.0038)

SAT Read

0.0343**
(0.0158)

SAT Write

-0.0442***
(0.0165)

Constant

0.7294***

0.6387***

1.5181***

0.3791***

0.6320**

(0.055672)

(0.0664)

(0.1580)

(0.0396)

(0.3174)

Number of observations

5,949

4,907

4,786

2,558

986

R-squared

0.05398

0.0671

0.06993

0.0211

0.009382

Coefficients estimated using Ordinary Least Squares with a linear regression model, with standard errors in
parenthesis. Statistical significance indicator using asterisk next to the coefficients, with * = significant at 10%, ** =
significant at 5%, and *** = significant at 1%.

7
8

Reference category for ethnicity is white
Reference category for high school rank is 0% to 10%

© 2013 Hanover Research | Academy Administration Practice

22

Hanover Research | April 2013

REGRESSION RESULTS: SET 2 MODELS
Figure 2.2, which includes Models 6, 7, 8, and 9, illustrates the relationship of various
factors on the conditional retention of students in their third, fourth, fifth, and sixth year at
Clarion. Once again, recall that conditional retention only looks at the retention of students
in a given year if they were retained in the previous year.

KEY FINDINGS ACROSS SET 2 MODELS
In terms of broad findings across the Set 2 models, we note that the following factors are
associated with a higher likelihood of being retained for one additional year (if the student
had been retained in the previous year):





Having a higher first year GPA
Being a student athlete in the previous year
Having attempted a higher number of credits in the previous semester

Additionally, having failed specific courses is associated with a lower likelihood of retention
for one additional year. These include ENG 111 (third and fourth year retention), MATH 050
(third and fourth year retention), MATH 110 (third year retention), and MATH 112 (third and
fifth year retention).

MODEL 6 (3RD YEAR CONDITIONAL RETENTION)



Compared to white students, black students who were retained in their second year
at Clarion are less likely to be retained in their third year at Clarion.



SAT score was found to have a negative relationship with conditional retention.
However, similar to our other findings surrounding SATs, the effect appears small. A
100-point increase in SAT score corresponds to a 1.05 percent decrease in the
likelihood of third year retention.



Students with higher first year GPAs at Clarion are more likely to be retained in their
third year than students with lower GPAs.



Students who failed ENG 111, MATH 050, MATH 110, and MATH 112 in the previous
year are less likely to be retained than students who did not attempt these courses.



Students who were listed as athletes in their second year at Clarion are more likely
to be retained in their third year than non-athletes.



Attempting more credits during the previous spring semester increases a student’s
likelihood of being retained in the third year. A one-credit increase is associated with
a 4.07 percent boost in the likelihood of retention (so a three-credit increase would
translate to a 12 percent boost). By contrast, the number of credits earned in the
previous fall semester was found to have a negative impact on likelihood of
retention. However, this effect was small (a one-credit increase in the number of
credits earned in the fall was associated with a 0.61 percent decrease in the

© 2013 Hanover Research | Academy Administration Practice

23

Hanover Research | April 2013

likelihood of retention), and this finding did not hold for retention in the fourth,
fifth, and sixth years (Models 7-9).

MODEL 7 (4TH YEAR CONDITIONAL RETENTION)



Among students who were retained in their third year, older students are less likely
to be retained in their fourth year than younger students.



SAT score also has a negative effect on the likelihood of retention, though once
again, the magnitude of this effect appears small in practical terms. A 100-point
increase in SAT scores is associated with a 0.63 percent decrease in the likelihood a
student will be retained in their fourth year.



Students with higher first year GPAs are more likely to be retained in their fourth
year.



Compared to students who did not attempt ENG 111 and MATH 050, students who
failed these courses have a lower chance of being retained in their fourth year.



Students who passed MATH 110 and MATH 112 in the previous year are expected to
have a higher retention rate than students who did not attempt these courses.



Students who were listed as athletes in their third year are more likely to be
retained in their fourth year.



Students who attempted more credits in the previous spring semester are more
likely to be retained in their fourth year.

MODEL 8 (5TH YEAR CONDITIONAL RETENTION)




Black students are less likely to be retained in their fifth year than white students.



First year GPA has a positive impact on students’ fifth year conditional retention. As
a student’s first GPA increases, he or she is more likely to be retained in the fifth
year.



Students who failed MATH 112 are less likely to be retained in their fifth year than
students who did not attempt the course.



Students who attempted more credits in the previous spring semester are more
likely to be retained in their fifth year.

Similar to Model 6, higher SAT scores are associated with a lower likelihood of being
retained in the fifth year. A 100-point increase in SATs is associated with a 1.57
percent decrease in the likelihood of retention at this stage.

MODEL 9 (6TH YEAR CONDITIONAL RETENTION)



An increase in high school rank as a percentage of class size is associated with a
decrease in the likelihood that a student would be retained in the sixth year. In
other words, lower ranked students (e.g., a student ranked 75th out of 100) are less

© 2013 Hanover Research | Academy Administration Practice

24

Hanover Research | April 2013

likely to be retained in the sixth year than students with higher ranks (e.g., a student
ranked at 25th out of 100).



Number of credits attempted in the previous spring semester has a positive impact
on students’ sixth year retention. Students who attempted more credits in the
previous spring are more likely to be retained in their sixth year.
Figure 2.2: Regression Output Results - Set 2
MODEL 6
RD
3 YEAR
CONDITIONAL

MODEL 7
TH
4 YEAR
CONDITIONAL

MODEL 8
TH
5 YEAR
CONDITIONAL

MODEL 9
TH
6 YEAR
CONDITIONAL

RETENTION

RETENTION

RETENTION

RETENTION

Age

-0.0286***
(0.0091)

Black

9

Other of two or more races

9

SAT

-0.0595***

-0.1337***

(0.021)

(0.044)

-0.0140

-0.0624

(0.0301)

(0.073)

-0.0105***

-0.0063**

-0.0157**

(0.003)

(0.003)

(0.0061)

High School Rank as percentage of class size

-0.4786***
(0.1566)

First GPA
ENG 111 in the previous year: Failed

10

ENG 111 in the previous year: Passed

10

MATH 050 in the previous year: Failed

11

MATH 050 in the previous year: Passed
MATH 110 in the previous year: Failed

12

MATH 110 in the previous year: Passed
MATH 112 in the previous year: Failed

11

12

13

0.0421***

0.0281***

0.0461***

(0.0082)

(0.0077)

(0.0162)

-0.1564***

-0.1074***

(0.0289)

(0.0331)

-0.0184

0.0089

(0.0168)

(0.0197)

-0.1854***

-0.1779***

(0.0364)

(0.0397)

-0.0151

-0.0059

(0.0145)

(0.0136)

-0.1475***

-0.0367

(0.0283)

(0.0259)

-0.0186

0.0353***

(0.0155)

(0.0135)

-0.0837***

-0.0256

-0.1296**

9

Reference category for ethnicity is white
Reference category for ENG 111 is did not attempt
11
Reference category for MATH 050 is did not attempt
12
Reference category for MATH 110 is did not attempt
13
Reference category for MATH 112 is did not attempt
10

© 2013 Hanover Research | Academy Administration Practice

25

Hanover Research | April 2013

MODEL 6
RD
3 YEAR
CONDITIONAL

MODEL 7
TH
4 YEAR
CONDITIONAL

MODEL 8
TH
5 YEAR
CONDITIONAL

MODEL 9
TH
6 YEAR
CONDITIONAL

RETENTION

RETENTION

RETENTION

RETENTION

(0.0302)

(0.0282)

(0.0549)

0.0173

0.0452***

0.036

(0.0132)

(0.0114)

(0.0266)

0.0641***

0.0355*

(0.0182)

(0.0182)

0.0407***

0.0303***

0.0294***

0.0301***

(0.0012)

(0.0011)

(0.0026)

(0.0057)

0.4165***

1.0009***

0.5613***

0.5099***

(0.0588)

(0.1801)

(0.0917)

(0.1067)

Number of Observations

3,904

2,538

841

173

R-squared

0.3284

0.278

0.1793

0.1809

MATH 112 in the previous year: Passed
Athlete in the previous year

13

14

Credits attempted in the last spring semester
Credits earned in the last fall semester

-0.0061**
(0.0028)

Constant

Coefficients estimated using Ordinary Least Squares with a linear regression model, with standard errors in
parenthesis. Statistical significance indicator using asterisk next to the coefficients, with * = significant at 10%, ** = significant
at 5%, and *** = significant at 1%.

REGRESSION RESULTS: SET 3 MODELS
Finally, we present the results to the regression models that estimate the variations in
students’ four-year and five-year graduation rates.

MODEL 10 (GRADUATION WITHIN FOUR YEARS)

14



In contrast to the relationship of age with retention, older students are more likely
to graduate within four years than younger students.



Black students are less likely to graduate within four years than their white
classmates.



Students in the top 25 percent of their high school class are more likely to graduate
within four years than other students. Once again, outside of the top 25 percent, the
lower a student ranks, the less likely they are to graduate within four years.



First year GPA is positively associated with the likelihood of graduating. In fact, a
one-point increase in GPA (e.g., 2.0 to 3.0) is associated with a 15.21 percent
increase in the likelihood of graduating within four years.



Total SAT score did not have a significant effect on graduation. However, individual
tests did have such an effect. More specifically, SAT Math and SAT Writing scores

Reference category is not an athlete

© 2013 Hanover Research | Academy Administration Practice

26

Hanover Research | April 2013

had a positive relationship (a 100-point increase in Math is associated with a 2.34
percent increase in likelihood of graduation, while a 100-point increase in Writing is
expected to yield a 3.96 percent increase in the likelihood of graduation). By
contrast, a 100-point increase in SAT Reading scores is associated with a 6.25
percent decrease in the probability of graduating in four years.

MODEL 11 (GRADUATION WITHIN FIVE YEARS)



Female students are 10.13 percent more likely to graduate within five years than
male students. Notably, this was the only model in which gender was found to have
a significant effect on graduation (or retention).




Black students are less likely to graduate within five years than white students.



First year GPA of students has a positive impact on the probability of five-year
graduation. A one-point increase in first GPA is associated with an 8.27 percent
increase in the likelihood of graduating within five years.



SAT Reading scores have a negative relationship with the likelihood of graduating
within five years. A 100-point increase in SAT Reading scores is associated with a
4.71 percent decrease in the probability of graduation.



Athletes are 8.6 percent more likely to graduate within five years than non-athletes.

Students in the top half of their high school class are more likely to graduate within
five years compared to students in the bottom 50 percent of their class. Outside of
the top 50 percent, the lower a student’s high school rank, the lower their likelihood
of graduating within five years.

Figure 2.3: Regression Output Results - Set 3
MODEL 10

MODEL 11

GRADUATION WITHIN 4 YEARS

GRADUATION WITHIN 5 YEARS

Female

0.1013***
(0.0332)

Age

-1.4003***
(0.377)

Age squared

0.0388***
(0.0101)

Black

15

Other or two or more races

15

High School Rank (10%-25%)

15
16

16

-0.1153***

-0.1331*

(0.0316)

(0.0699)

-0.0445

-0.0582

(0.0491)

(0.1141)

-0.0507

-0.0794

Reference category for ethnicity is white
Reference category for high school rank is 0% to 10%

© 2013 Hanover Research | Academy Administration Practice

27

Hanover Research | April 2013

High School Rank (25%-50%)

16

High School Rank (50%-75%)

16

High School Rank (75%-100%)
SAT Math

16

MODEL 10

MODEL 11

GRADUATION WITHIN 4 YEARS

GRADUATION WITHIN 5 YEARS

(0.0328)

(0.0659)

-0.1218***

-0.0945

(0.0317)

(0.064)

-0.1872***

-0.168**

(0.0347)

(0.0708)

-0.2362***

-0.2406***

(0.0414)

(0.0864)

0.0234*
(0.0125)

SAT Read
SAT Write

-0.0625***

-0.0471**

(0.0154)

(0.0219)

0.0396**
(0.0153)

First GPA

0.1521***

0.0827***

(0.009)

(0.0258)

Athlete

0.086**
(0.0371)

Constant

12.7073***

0.853***

(3.5365)

(0.15)

Number of Observations

2,839

604

R-squared

0.2112

0.1118

Coefficients estimated using Ordinary Least Squares with a linear regression model, with standard errors in
parenthesis. Statistical significance indicator using asterisk next to the coefficients, with * = significant at 10%, ** =
significant at 5%, and *** = significant at 1%.

© 2013 Hanover Research | Academy Administration Practice

28

Hanover Research | April 2013

APPENDIX A: RETENTION R ATES
Figure A.1: Retention Rates by Gender
80%

72%

67%
61%

60%

55%

54%

40%

50%

32%

31%

20%
9%

8%
0%
Female
2nd year retention

Male

3rd year retention

4th year retention

5th year retention

6th year retention

Figure A.2: Conditional Retention by Gender
100%

90%
83%

90%

85%

81%

79%

80%
61%

53%

60%
40%
20%
0%
Female

Male

3rd year conditional retention

4th year conditional retention

5th year conditional retention

6th year conditional retention

© 2013 Hanover Research | Academy Administration Practice

29

Hanover Research | April 2013

Figure A.3: Retention Rate by Ethnicity
80%

60%

71%
61%

60%

57%

55%
48%

40%

40%

41%
36%

33%
22%

21%

20%

10%

8%

9%

0%
Black
2nd year retention

Other or two or more races
3rd year retention

4th year retention

White

5th year retention

6th year retention

Figure A.4: Conditional Retention Rate by Ethnicity
100%
80%
65%

91%

89%

82%

77%

71%

83%

80%

85%

67%
59%

60%

56%

40%
20%
0%
Black

Other or two or more races

White

3rd year conditional retention

4th year conditional retention

5th year conditional retention

6th year conditional retention

© 2013 Hanover Research | Academy Administration Practice

30

Hanover Research | April 2013

Figure A.5: Retention Rate by High School Class Rank
100%
83%
80%

81%

74%

74%
72%

69%

68%

63%
58%

60%

36%

40%

63%
55%

51%
44%

41%

38%
34%

36%
29%

22%
20%

15%
9%

9%

5%

5%
0%
0% to 10%
2nd year retention

10% to 25%
3rd year retention

25% to 50%
4th year retention

50% to 75%
5th year retention

75% to 100%
6th year retention

Figure A.6: Conditional Retention Rate by High School Class Rank
100%

94%
91%

86%

80%

95%
87%
87%

91%
84%

85%

84%

82%

76%

74%
67%

86%

65%

75%
62%

66%

60%

40%
23%
20%

0%
0% to 10%

10% to 25%

25% to 50%

50% to 75%

75% to 100%

3rd year conditional retention

4th year conditional retention

5th year conditional retention

6th year conditional retention

© 2013 Hanover Research | Academy Administration Practice

31

Hanover Research | April 2013

APPENDIX B: GRADUATION RATES
Figure B.1: Graduation Rate by Gender
80%
62%
60%

40%

52%
37%
29%

20%

0%
Female

Male

Four year graduation rate

Five year graduation rate

Figure B.2: Graduation Rate by Ethnicity
80%

37%

40%

20%

59%

57%

60%

35%

33%

15%

0%
Black

Other or two or more
races

Four year graduation rate

© 2013 Hanover Research | Academy Administration Practice

White

Five year graduation rate

32

Hanover Research | April 2013

Figure B.3: Graduation Rate by High School Class Rank
100%
81%

75%

80%

63%

61%
60%

50%

45%
37%

40%

28%

22%
20%

12%

0%
0% to 10%

10% to 25%

25% to 50%

Four year graduation rate

© 2013 Hanover Research | Academy Administration Practice

50% to 75%

75% to 100%

Five year graduation rate

33

Hanover Research | April 2013

PROJECT EVALUATION FORM
Hanover Research is committed to providing a work product that meets or exceeds partner
expectations. In keeping with that goal, we would like to hear your opinions regarding our
reports. Feedback is critically important and serves as the strongest mechanism by which we
tailor our research to your organization. When you have had a chance to evaluate this
report, please take a moment to fill out the following questionnaire.
http://www.hanoverresearch.com/evaluation/index.php

CAVEAT
The publisher and authors have used their best efforts in preparing this brief. The publisher
and authors make no representations or warranties with respect to the accuracy or
completeness of the contents of this brief and specifically disclaim any implied warranties of
fitness for a particular purpose. There are no warranties which extend beyond the
descriptions contained in this paragraph. No warranty may be created or extended by
representatives of Hanover Research or its marketing materials. The accuracy and
completeness of the information provided herein and the opinions stated herein are not
guaranteed or warranted to produce any particular results, and the advice and strategies
contained herein may not be suitable for every partner. Neither the publisher nor the
authors shall be liable for any loss of profit or any other commercial damages, including but
not limited to special, incidental, consequential, or other damages. Moreover, Hanover
Research is not engaged in rendering legal, accounting, or other professional services.
Partners requiring such services are advised to consult an appropriate professional.

© 2013 Hanover Research | Academy Administration Practice

34

Hanover Research | April 2013

1750 H Street NW, 2nd Floor
Washington, DC 20006

P 202.756.2971 F 866.808.6585
www.hanoverresearch.com

© 2013 Hanover Research | Academy Administration Practice

35